Short-Term SPY ETF Price Forecasting Model

Introduction

This project aims to predict the short-term high and low prices of the SPY ETF using various machine learning and statistical models. The primary objective is to aid retail traders, particularly day traders, in making informed investment decisions.

Data

The project utilizes historical price data and a suite of technical indicators as input for the models. Different Jupyter Notebooks come with their corresponding datasets.

Models

Several models were implemented and tested in this project:

• Baseline Models:

  • Linear Regression
  • Ridge Regression
  • Lasso Regression

• Advanced Models:

  • Random Forest
  • Support Vector Regression (SVR)
  • Extreme Gradient Boosting (XGBoost)

• Time-Series Models:

  • Long Short-Term Memory (LSTM)
  • Hidden Markov Models (HMM)

Notebooks

• .ipynb: Use Baseline Models and Advanced Models to predict next one hour price for SPY.

• HMM.ipynb: Use HMM model to predict next one hour price for SPY.

• LSTM.ipynb: Train LSTM model to predict next one hour high price for SPY.

Authors

Team Algebros: Sailun Zhan, Xinwu Yang, Aolong Li, Amin Idelhaj, Zongze Liu

License

This project is licensed under the MIT License - see the LICENSE file for details.